With the revolution of digitizing communications ever closer to the antenna, SDR with programmable modulation schemes and transmission rates has become a practical wireless communication platform. Recently, modulation classification techniques have attracted much attention in SDR applications to develop cognitive radios with automatic modulation scheme recognition and selection. The concept is to exploit the radio transmission environment and choose the best modulation scheme to maximize the channel capacity in real time. The general principles of SDRs and modulation classifications or schemes are presented in the following publications which are incorporated herein in their entirety: Y. Huang and A. Polydoros, Likelihood methods for MPSK modulation classification. IEEE Trans. Commun., vol. 43, 1493-1504; J. Sills, Maximum-likelihood modulation classification for PSK/QAM. Proc. MILCOM '99, 1999, 57-61; K. Umebayshi et al., “Blind estimation of the modulation scheme adapted to noise power based on a concept of software define radio,” in Proc. in European Wireless 2002(EW2002), pp. 829-834 (2002-02); O. Menguc and F. Jondral, “Air interface recognition for a software radio system exploiting cyclostationarity,” in Proc. of the 15th IEEE Personal, Indoor and Mobile Radio Communications, Vol. 3, September 2004, pp. 1947-1951. The concept of automatic modulation classification is also discussed in “Real-time Modulation Classification Based on Maximum Likelihood,” by Wei Su, Jefferson L. Xu and Mengchu Zhou. This publication was presented to the IEEE in about November 2008 and is incorporated herein in its entirety. Further discussion of software-defined radio and modulation recognition is presented in “Software Defined Radio Equipped with Rapid Modulation Recognition” by Jefferson L. Xu, Wei Su, Senior Member, IEEE and Mengchu Zhou, Fellow, IEEE, which is also incorporated herein in its entirety. The latter publication was also presented to the IEEE in about October 2009.
In cognitive radio, the signal data can be transmitted frame by frame. The modulation scheme in each data frame was determined depending upon the channel condition estimated before the transmission. The adaptive modulation scheme maintains the bit-error rate (BER) below a certain threshold to ensure the quality of the service in data transmission. To do this, a known pilot symbol is used in the transmitted data frame to indicate the modulation scheme for proper demodulation of the receiver.
Recently, some research has been done on migrating the existing automatic modulation classification algorithms to recognize the modulation scheme change automatically without the redundant pilot symbols. A common practice is to have a modulation estimator to operate in parallel with a programmable demodulator in the receiver. A change in the modulation scheme of the transmitter in each data frame will be detected and the demodulator will be cued to change the demodulation scheme accordingly. The objective of the automatic modulation recognition is to identify the modulation scheme of a transmitted signal with a high probability of success within a short observation time. The popular algorithms are maximum likelihood based approaches such as average likelihood ratio test (ALRT) and generalized likelihood ratio test (GLRT), high-order statistics based approaches such as moments and cumulants, cyclic frequency based approaches, and some other approaches such as zero-crossing and spectral correlation etc.
Most of the existing practical automatic modulation classification methods have been developed for military applications which are time-consuming and computationally intensive in order to meet the blind and non-cooperative signal collection environment. However, the commercial cognitive radios operate in the quite different environment and face the different challenges. The prompt and low-cost processing are the key to success and new techniques are needed to meet the real-time demodulation capability.